Sunday, October 01, 2017

Sunday Morning Insight: The Demise of Cassini and the Rise of Artificial InteIligence

In the past few weeks, two events connected to the whole Artificial Intelligence narrative occurred: Cassini plunged into Saturn while NIPS conference registrations closed in an, unheard of, record amount of time.

Pretty often the Artificial Intelligence narratives revolve around one factor and then explains away why the field cannot go on because that factor is not new, not good anymore, not whatever...... That sort of narrative was pushed by Tech Review when it mentioned that AI may be plateauing because "Neural Networks" are thirty or more years old. Yes, neural networks have existed for a long time and no AI is not going to be plateauing because it actually hinges on several factors, not one.

This is the story of one of these factors.

It started thanks in large part to Space exploration, and no, we are not talking about the awesome Deep Space 1 spacecraft [1] even though much like that spacecraft, that story also started at JPL.

Without finding ways to cut costs substantially, JPL faced extinction. The NASA budget would not support enough Cassini-scale missions to keep the lab operating.

The vast majority of cameras in space missions had, until then, used CCD devices. While the technology provided high quality images, it was brittle. For one, it required cooling to get some good signal over noise ratio. That cooling in turn meant that the imagers required more power to operate and could fail more systematically during launch phases. It was also a line based design meaning that you could lose an entire line of pixels at once. In short, it was fragile and more importantly the technology made the sensor heavier, a cardinal sin in Space Exploration.

....One of the instrument goals was to miniaturize charge-coupled device (CCD) camera systems onboard interplanetary spacecraft. In response, Fossum invented a new CMOS active pixel sensor (APS) with intra-pixel charge transfer camera-on-a-chip technology, now just called the CMOS Image Sensor or CIS[5][6] (active pixel sensors without intra-pixel charge transfer were described much earlier, by Noble in 1968.[7] As part of Goldin's directive to transfer space technology to the public sector whenever possible, Fossum led the CMOS APS development and subsequent transfer of the technology to US industry, including Eastman Kodak, AT&T Bell Labs, National Semiconductor and others. Despite initial skepticism by entrenched CCD manufacturers, the CMOS image sensor technology is now used in almost all cell-phone cameras, many medical applications such as capsule endoscopy and dental x-ray systems, scientific imaging, automotive safety systems, DSLR digital cameras and many other applications.

Since CMOS rely on the same process as used in computing chips, it scaled big time and became very cheap. In fact, the very creation of massive image and video collections of datasets hosted by the likes of YouTube then Google, Flickr then Yahoo!, InstaGram then Facebook and most other internet companies, was uniquely enabled by the arrival of CMOS in consumer imaging, first in cameras and then in smartphones:

The size of these datasets enabled the ability to train very large neural networks beyond toy models. New algorithm developments on top of neural networks and large datasets brought error rates down to the point where large internet companies could soon begin to utilize these techniques on the data that had been collected since the early 2000's on their servers.

On September 14th 2017, Cassini was downloading it's last CCD-based images and all the registration at NIPS, one of the most well known ML/DL/AI conference, sold out three months ahead of the meeting: a feat that is unheard of for a specialist's conference. The conference will be held in Long Beach, not far from JPL where, somehow, the sensor that started it all, was born.

[2] The TRL scale that everyone uses these days, ( and translated for the first time in French was here on Nuit Blanche) was born around that time so that NASA could evaluate what technology could be integrated faster into space missions.